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10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
1
Chapter 10
Methods for Environment–Productivity Trade-‐off Analysis in Agricultural Systems
M.T. van Wijk3, C.J. Klapwijk1,2*, Todd S. Rosenstock4, Piet J.A. van Asten2, Philip K.
Thornton5 and Ken E. Giller1
Abstract Trade-‐off analysis has become an increasingly important approach for
evaluating system level outcomes of agricultural production and for prioritizing and
targeting management interventions in multifunctional agricultural landscapes. We
review the strengths and weakness of different techniques available for performing
trade-‐off analysis. These techniques, including mathematical programming and
participatory approaches, have developed substantially in recent years aided by
mathematical advancement, increased computing power, and emerging insights into
systems behaviour. The strengths and weaknesses of the different approaches are
identified and discussed, and we make suggestions for a tiered approach for
situations with different data availability.
*C.J. Klapwijk
Plant Production Systems Group, Wageningen University
Wageningen, The Netherlands
Email: [email protected]
1 Plant Production Systems Group, Wageningen University, the Netherlands 2 International Institute of Tropical Agriculture, Kampala, Uganda 3 International Livestock Research Institute, Nairobi, Kenya 4 World Agroforestry Centre, Nairobi, Kenya
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
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5 CGIAR Research Program on Climate Change, Agriculture and Food Security,
Nairobi, Kenya
10.1 Introduction
Trade-‐offs, by which we mean exchanges that occur as compromises, are ubiquitous
when land is managed with multiple goals in mind. Trade-‐offs may become
particularly acute when resources are constrained and when the goals of different
stakeholders conflict (Giller et al. 2008). In agriculture, trade-‐offs between output
indicators may arise at all hierarchical levels, from the crop (such as grain versus crop
residue production), the animal (milk versus meat production), the field (grain
production versus nitrate leaching and water quality), the farm (production of one
crop versus another), to the landscape and above (agricultural production versus
land for nature). An individual farmer may face trade-‐offs between maximizing
production in the short term and ensuring sustainable production in the long term.
Within landscapes, trade-‐offs may arise between different individuals for competing
uses of land. Thus trade-‐offs exist both within agricultural systems, between
agricultural and broader environmental or sociocultural objectives, across time and
spatial scales, and between actors. Understanding the system dynamics that
produce and change the nature of the trade-‐offs is central to achieving a sustainable
and food secure future.
In this chapter we focus on how the complex relationships between the
management of farming systems and its consequences for production and the
environment — here represented by greenhouse gas emissions — can be analysed
and how trade-‐offs and possible synergies between output indicators can be
quantified. For example, an important hypothesis is that by increasing soil carbon
sequestration in agricultural systems, farmers can generate a significant share of the
total emission reductions required in the next few decades to avoid catastrophic
levels of climate change. At the same time, increasing soil carbon sequestration also
increases soil organic matter, which is fundamental to improving the productivity
and resilience of cropping and livestock production systems, and thereby a potential
win–win situation is identified. However, it is debatable whether these win–win
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
3
situations exist in reality. An important constraint for this hypothesis is the lack of
organic matter like crop residues on many smallholder mixed crop–livestock
systems, to serve both as feed for livestock and as an input into the soil in order to
increase soil organic matter. This organic matter could be produced through the use
of mineral fertilizer or intensification of livestock production, but both of these have
negative consequences for greenhouse gas emissions, probably offsetting the gains
made in soil organic matter storage. It therefore seems likely that to achieve
maximum impact on smallholders’ food production and food security, environmental
indicators have to be compromised. However, good quantitative insight into these
compromises is still lacking.
Trade-‐off analysis has emerged as one approach to assessing farming system
dynamics from a multidimensional perspective. Although the concept of trade-‐offs
and their opposite — synergies — lies at the heart of several current agricultural
research for development initiatives (Vermeulen et al. 2011; DeFries and Rosenzweig
2010), methods to analyse trade-‐offs within agro-‐ecosystems and the wider
landscape are nascent (Foley et al. 2011). We review the state of the art for trade-‐off
analyses, highlighting important innovations and constraints, and discuss the
strengths and weaknesses of the different approaches used in the current literature.
10.2 The Nature of Trade-‐off Analysis
Trade-‐offs are quantified through the analysis of system-‐level inputs and outputs
such as crop production, household labour use, or environmental impacts such as
greenhouse gas emissions. The outcomes that different actors may want to achieve,
in and beyond the landscape, need to be defined at different time and spatial scales.
Understanding these desired outcomes, or different stakeholders’ objectives, is a
necessary first step in trade-‐off analysis.
We illustrate the key concepts and processes of trade-‐off analysis with a simple
example that has only two objectives: farm-‐scale production and an environmental
impact, greenhouse gas emissions. Once the objectives have been defined, the next
step is to identify meaningful indicators that describe these objectives. The
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
4
indicators form the basis for characterizing the relationships between objectives (Fig.
1). The shape of the trade-‐off curve gives important information on the severity of
the trade-‐off of interest. Is it simply a straight line, like the central curve (Fig. 1a)? Is
the curve convex (i.e. the lower curve), which means strong trade-‐offs exist between
the indicators); or concave (i.e. the upper curve), which means the indicators are
independent of each other and the trade-‐offs between the indicators are quite
‘soft’? The shape of the trade-‐off curve represents different functional relationships
and can be assessed by evaluating farm management options; in our example, each
point could represent a method and level of mineral fertilizer application (Fig. 1b).
The position of each option in the trade-‐off space describes its outcomes in terms of
the two indicators, productivity and environmental impact. Based on this
information, a ‘best’ trade-‐off curve can be drawn (Fig. 1c). In trade-‐off analyses the
researcher will be interested in which system management interventions result in
which type of outcome of the different objectives (Fig. 1d).
Once the best (observed or inferred) trade-‐off curve has been identified, various
system management interventions can be studied to assess the extent to which they
contribute to the desired objectives (Fig. 1d). This analysis determines whether so-‐
called ‘win–win’ solutions are possible, where the performance of the system can be
improved with regard to both objectives. Alternatively, does improvement in one
objective automatically lead to a decrease in system performance for another
objective (Fig. 1e)? Possible threshold values can be identified once the shape of the
trade-‐off curve is known. For example, do productivity thresholds exist, above which
the environmental impact increases rapidly? In some situations, it may be possible to
alter the nature of the trade-‐off between production and environmental impact
through the exploration of new management interventions (Fig. 1f), thereby
redefining the ‘best’ trade-‐off curve.
10.3 Research Approaches and Tools
Trade-‐offs are typically much more complex with more dimensions and objectives
than indicated by the simple two-‐dimensional example presented in the previous
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
5
section. A wide variety of tools and approaches have been developed to account for
diverse situations. The most suitable approach depends on the nature and scale of
the problem to be addressed, the trade-‐offs involved, and the indicators available.
We assess five widely applied approaches: (i) participatory methods; (ii) empirical
analyses; (iii) econometric tools; (iv) optimization models and (v) simulation models.
These five approaches overlap often and can help generate complementary
knowledge. Consequently, trade-‐off analyses will often utilize several methods
simultaneously or iteratively.
The concept of participatory research originally highlighted the need for the active
involvement of those who are the subject of research, or for whom the research may
lead to outcome changes. In recent times, the notion has expanded to acknowledge
that change in researchers’ assumptions and perceptions may be required to achieve
desired outcomes that are attractive to farmers (Crane 2010). Participatory
approaches, such as fuzzy cognitive mapping (Murungweni et al. 2011), resource
flow mapping, games and role-‐playing, are powerful ways to identify actor-‐relevant
objectives and indicators, although the scope of farmer knowledge and perceptions
within scientific research can be constraining in some situations, particularly in times
of rapid change (Van Asten et al. 2009). There are many examples of participatory
approaches (Gonsalves 2013) that could be or are used to assess trade-‐offs.
Participatory approaches usually generate qualitative data and so, although they
may not be well suited for quantifying trade-‐offs, they provide critically important
information to support quantitative tools, for example through the development of
participatory scenarios (DeFries and Rosenzweig 2010; Claessens et al. 2012).
However, despite the participatory nature of these approaches, the assessment of
trade-‐offs often remains researcher-‐driven.
Quantitative assessment of trade-‐offs requires empirical or experimental approaches
to generate data on the behaviour of the system under different conditions. Trade-‐
off curves can be drawn on the basis of experimental measurements of indicators,
such as the removal of plant biomass for fodder and the resulting soil cover, which is
a good proxy for control of soil erosion (Naudin et al. 2012). Statistical techniques
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
6
such as data envelope analysis (Fraser and Cordina 1999) or boundary line analysis
(Fermont et al. 2009) can be used to quantify best possible trade-‐offs between
indicators in empirical datasets (e.g. Fig 1c). Related to these empirical approaches
are econometric tools: these use large datasets as the basis of statistical coefficients
that define the input–output relationships of system level outcomes (e.g. Antle and
Capalbo 2001). Developments combine biophysical and socioeconomic aspects of
the system, and use farm-‐level responses to quantify consequences at a regional
level (Antle and Stoorvogel 2006). Empirical and econometric approaches are
powerful in the sense that outcomes of various system choices can be explored using
the existing variability in system configuration and performance. However, the
inference space of the analysis is constrained to the dataset collected and is
therefore not suitable for predicting outcomes outside the ranges of the original
data.
Empirical approaches cannot be used to assess indicators that are difficult to
measure directly; therefore, they are often combined with simulation models to
obtain an overview of overall system performance. Simulation models allow the
dynamic nature of trade-‐offs to be explored, where outcomes can differ in the short
or long term (Zingore et al. 2009). System performance, expressed quantitatively in
terms of outcomes represented by different indicators, can be used as an input for
optimization approaches such as mathematical programming (MP). MP finds the
best possible trade-‐off through multicriteria analysis and can assess whether this
trade-‐off curve can be alleviated through new interventions. MP has a long history
(e.g. Hazell and Norton 1986) and is among the most extensively used trade-‐off
application in land use studies (e.g. Janssen and Van Ittersum 2007). This is despite
its inherent limitation, that land users do not always behave according to economic
rationality and optimize their behaviour. Techniques have been developed recently
to solve non-‐linear MP problems and integrate across levels, linking farms and
regions through markets and environmental feedbacks (e.g. Laborte et al. 2007;
Roetter et al. 2007; Louhichi et al. 2010).
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
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Inverse modelling techniques use non-‐linear simulation models directly to perform
multiobjective optimization without the intermediate step of MP. Furthermore, with
the identification of the appropriate model outputs, system behaviour can be
assessed across different temporal and spatial scales and feedbacks taken into
account, which is often a weak part of MP models. The complexity of agro-‐
ecosystems and the large number of potential indicators can hamper efficient
applications of this computationally intensive method. But advances in computer
power have resulted in several applications in farming systems research, going from
farm to landscape (Groot et al. 2012; Groot et al. 2007; Tittonell et al. 2007).
The various approaches to trade-‐off analysis each have key strengths and
weaknesses and combining approaches may provide enhanced opportunities for a
realistic, relevant and integrated assessment of systems (Table 1). For example, in
many cases participatory approaches are needed to define meaningful objectives
and indicators, but are not suitable to reliably quantify the trade-‐offs associated with
possible interventions. Empirical and econometric approaches can be used to
quantify the current state of the overall agricultural system. In many cases, however,
simulation models are needed to quantify indicators that are difficult to measure
(for example, effects of management on longer term productivity) and to explore
options beyond the existing system configurations and boundaries (Table 1).
Optimization can used to assess the potential for synergies and alleviation of trade-‐
offs, but has limited applicability when sociocultural traditions and rules play a key
role (Thornton et al. 2006).
It is clear that for trade-‐off analyses combinations of techniques are needed.
Multicriteria analysis is an example of such an integrated approach, in which
participatory and optimization methods are combined: the weighting of the
individual criteria in goal programming models is done together with the
stakeholders, and by changing these weights with the stakeholders a trade-‐off
analysis is performed (e.g. Romero and Rehman 2003).
10.4 A Tiered Approach
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
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The discussion above demonstrates that for fully integrated trade-‐off analyses
different approaches should be combined. However, in many cases data availability
will not allow such elaborate analyses. The techniques discussed in the previous
section not only have different strengths and weaknesses, but also different data
demands. Typically, empirical and econometric approaches are highly data
demanding, whereas participatory approaches can provide essential information
about system functioning after only a few well-‐designed discussion panels and
targeted questionnaires. Simulation and optimization models can be, in terms of
data demand, anywhere between these extremes. Their data demand is highly
determined by model setup and complexity.
An example of a tiered approach in which researchers move from quick initial data
analyses to more complex, data demanding, modelling exercises is the four step
approach used by Van Noordwijk and his team at ICRAF (Meine van Noordwijk,
personal communication; see also Tata et al. 2014 for the first three steps; Villamor
et al (2014) for an agent-‐based modelling approach).
• Step 1 is the collection of system characterization data and the analysis of
these data to explore whether trade-‐offs can be identified, for example
between an environmental indicator like soil carbon and the net present
value of the land.
• The second step is to look at these variables from a dynamic perspective and
identify opportunities for interventions by analysing the opportunity costs of
different management options. This step already requires much more
detailed data than step 1, and in the example above, could be used to
identify the price of emission reduction potentials.
• In the third step, the consequences of the identified intervention options for
the different land users and the environment can be explored by using
dynamic land-‐use models.
• Finally in the fourth step, agent-‐based models and participatory modelling
exercises are used to analyse the opinions of, and interactions between,
different actors in the landscape. This provides an integrated analysis of both
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
9
the environmental and socioeconomic factors and actors within the
landscape.
This four-‐step approach demonstrates the way in which the strengths of different
methods of trade-‐off analysis can be combined, and how such an analysis can move
stepwise towards more complex and data-‐demanding exercises.
All in all it is not straightforward to give concrete advice that relates the purpose of
analysis to the technique and approach to be used. Researchers make personal
choices about complexity and analytical approach as part of the ‘art’ of modelling
and trade off analyses. This is sometimes difficult to reconcile with the ‘objectivity’
that we pursue in scientific research. However, some general indications can be
given.
If the objective of the analysis is to assess the overall potential for system
improvement and the room for manoeuvre to increase efficiencies and profitability
without negative effects on environmental indicators, then optimization approaches
are the most logical choice. If the purpose is to analyse the short-‐ and long-‐term
consequences of certain interventions and the trade-‐offs between different
objectives over different time scales, then simulation modelling is an obvious
candidate. This may be combined with some sort of multiobjective, non-‐linear
optimization or inverse modelling approach.
Both optimization and simulation are typically used for scientifically oriented studies.
In order to have real-‐life impact, that takes into account the complexities of
agricultural systems and the large diversity of drivers and options in agricultural land
use, especially in developing countries, a variety of quantitative and qualitative
approaches are likely to be needed (e.g. Murungweni et al. 2011). The setup of these
tools, the identification of indicators, and the presentation of results need to be
determined using participatory approaches where key stakeholders are involved and
drive decisions from the beginning of the project. This might lead to the study having
less value in terms of scientific novelty, but will increase its practical relevance on
the ground. With the topic of this chapter in mind, it is ironic that in many cases
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
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there might be a trade-‐off between the scientific and societal impact that can be
achieved by a research project that has its own constraints in terms of time and
money.
Acknowledgements
This study is an outcome of a workshop entitled ‘Analysis of Trade-‐offs in Agricultural
Systems’ organized at Wageningen University, February 2013. We thank all
participants for their discussions, which contributed strongly to the content of this
chapter. The workshop and subsequent work were funded by the CGIAR Research
Program on Climate Change, Agriculture and Food Security (CCAFS), Theme 4.2:
Integration for Decision-‐Making – Data and Tools for Analysis and Planning. This
chapter is a modified and extended version of Klapwijk et al. (2014).
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
11
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a b
c d
e f
Fig. 10.1 Key concepts of trade-‐offs and their analysis for a simple two objective
example (for explanation see text)
a Shape, b Outcomes of management options, c Trade-‐off and possibility for
synergies, d Strategies (interventions) and outcomes e Thresholds, f Can trade-‐off be
alleviated
EI Environmental Impact, P Production
P
EI
? ?
Shape
?
P
EI
Outcomes of management options
P
EI
Trade-off and possibility for synergies
P
EI
?
Strategies and outcomes?
?
?
Thresholds
P
EI
? ?
P
EI
Can trade-off be alleviated?
?
10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
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10 Methods for Environment – Productivity Trade-‐off Analysis in Agricultural Systems
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Table 10.1 Strengths and weaknesses of the different approaches for analysing trade-‐offs in agricultural systems
Research Approach
Empirical Econometric Simulation Optimization Participatory
Aspect Act Pot Act Pot Act Pot Act Pot Act Pot
Integration of interdisciplinary content -‐ + + + -‐ + -‐ -‐ -‐ +
Assessment across different time horizons -‐ -‐ + + + + + + -‐ +
Assessment across spatial scales and integration levels
-‐ + -‐ + +/-‐ +/-‐ +/-‐ + -‐ +
Takes into account qualitative information -‐ + -‐ -‐ -‐ -‐ -‐ -‐ + +
Appropriate representation of uncertainty -‐ + -‐ + -‐ + -‐ + -‐ +
Identification of possibilities to alleviate the observed trade-‐offs
-‐ -‐ -‐ -‐ + + + + -‐ -‐
Ability to deal with real-‐life system complexity + + -‐ + -‐ -‐ -‐ -‐ + +
Applicability to real-‐life decision-‐making + + + + -‐ -‐ +/-‐ +/-‐ + +
Act actual or current use in the scientific literature, Pot potential usefulness of technique